Adaptive Dispersion Network of Multiple Drones Based on Reinforcement Learning

Ryota Maikuma, Tenta Kawai, Mai Bando, Shinji Hokamoto

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

This study discusses how to construct an observation network consisting of drones in three-dimensional space. In the network, each drone adaptively changes its motion to disperse from other drones considering relative positions to others. Such movement is independently decided under a distributed control strategy without any communication to others. Since the environment perceived by a drone changes according to other drones' motion, reinforcement learning is used to generate an adaptive action of drones. Besides, to avoid the curse of dimensionality in reinforcement learning process, this study utilizes a policy-based strategy for the states and actions. This paper explains several key factors (state, reward, etc.) in the network construction problem. Some typical results of numerical simulations are shown and indicate the effectiveness of the key factors/setting in the learning process. Moreover, it is shown that the obtained variables in the learning process indicate effective actions for the dispersion network when the number of drones changes in a mission.

本文言語英語
ホスト出版物のタイトル10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024
出版社Institute of Electrical and Electronics Engineers Inc.
ページ223-228
ページ数6
ISBN(電子版)9798350373974
DOI
出版ステータス出版済み - 2024
イベント10th International Conference on Control, Decision and Information Technologies, CoDIT 2024 - Valletta, マルタ
継続期間: 7月 1 20247月 4 2024

出版物シリーズ

名前10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024

会議

会議10th International Conference on Control, Decision and Information Technologies, CoDIT 2024
国/地域マルタ
CityValletta
Period7/1/247/4/24

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ サイエンスの応用
  • 情報システム
  • 決定科学(その他)
  • 情報システムおよび情報管理
  • 制御およびシステム工学
  • 制御と最適化

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